Playing with dual purposes: A study of professional football players engagement in environmental advocacy and activism
Bibliographic record
Abstract
Despite a burgeoning literature on athlete activism and advocacy related to a range of social issues, research on athletes’ environmental engagement is still scarce—a surprising gap considering the rise of environmental movements around the globe and pressing concerns related to climate change in particular. Through interviews with 10 professional football players engaged in environmental activism and advocacy, this article helps fill this gap by exploring 1) how professional football players engage in environmental advocacy and activism, 2) ways the players perceive their role in supporting pro-environmental changes, and 3) paradoxes related to their engagement. Our analysis illustrates that although the players’ modes of engaging varied to some extent, they identified some common contradictions and tensions related to social, structural and categorical facets of their engagement. Players often negotiated a liminal position between, on one hand, their aspirations to do pro-environment work, and, on the other, their focus on athletic performance and the need to maneuver economic sustainability incentives that drive clubs, federations and the football industry. Grounded in Giulianotti et al.'s ( 2021 ) analytical framework on “liminal antinomies,” these negotiations are explored in depth, along with the modes of environmental engagement by players and their self-reflections. The liminal antinomies we identified are further discussed in relation to the literature on activism and social change through sports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".